
import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowercase    :   Optional[int]						=			get_tests_dir('fixtures/test_sentencepiece_no_bos.model')




@require_sentencepiece
@require_tokenizers
class 					lowerCamelCase__				(							lowercase__	,		unittest.TestCase):




	'''simple docstring'''




	_A								=	PegasusTokenizer
	_A								=	PegasusTokenizerFast
	_A								=	True
	_A								=	True







	def   _lowerCamelCase							(   self						:Optional[Any]		)						->					Optional[int]:
								super().setUp()

								# We have a SentencePiece fixture for testing
								__UpperCamelCase      :  Union[str, Any]       =   PegasusTokenizer(SCREAMING_SNAKE_CASE_		)
								tokenizer.save_pretrained(self.tmpdirname		)







	@cached_property
	def   _lowerCamelCase							(   self						:Any		)						->					List[str]:
								return PegasusTokenizer.from_pretrained("google/pegasus-large"		)







	def   _lowerCamelCase							(   self						:List[str]	,  **a						:int		)						->					PegasusTokenizer:
								return PegasusTokenizer.from_pretrained(self.tmpdirname	,  **SCREAMING_SNAKE_CASE_		)







	def   _lowerCamelCase							(   self						:List[str]	,  a						:List[str]		)						->					List[Any]:
								return ("This is a test", "This is a test")







	def   _lowerCamelCase							(   self						:Optional[int]		)						->					str:
								__UpperCamelCase      :  Optional[Any]       =   "</s>"
								__UpperCamelCase      :  List[Any]       =   1

								self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE_		)	,  SCREAMING_SNAKE_CASE_		)
								self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE_		)	,  SCREAMING_SNAKE_CASE_		)







	def   _lowerCamelCase							(   self						:str		)						->					Tuple:
								__UpperCamelCase      :  str       =   list(self.get_tokenizer().get_vocab().keys()		)

								self.assertEqual(vocab_keys[0]	,  "<pad>"		)
								self.assertEqual(vocab_keys[1]	,  "</s>"		)
								self.assertEqual(vocab_keys[-1]	,  "v"		)
								self.assertEqual(len(SCREAMING_SNAKE_CASE_		)	,  1_1_0_3		)







	def   _lowerCamelCase							(   self						:Tuple		)						->					Union[str, Any]:
								self.assertEqual(self.get_tokenizer().vocab_size	,  1_1_0_3		)







	def   _lowerCamelCase							(   self						:str		)						->					Optional[int]:
								__UpperCamelCase      :  int       =   self.rust_tokenizer_class.from_pretrained(self.tmpdirname		)
								__UpperCamelCase      :  str       =   self.tokenizer_class.from_pretrained(self.tmpdirname		)
								__UpperCamelCase      :  List[Any]       =   (
								    "Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"
								    " </s> <pad> <pad> <pad>"
								)
								__UpperCamelCase      :  Any       =   rust_tokenizer([raw_input_str]	,  return_tensors=SCREAMING_SNAKE_CASE_	,  add_special_tokens=SCREAMING_SNAKE_CASE_		).input_ids[0]
								__UpperCamelCase      :  Optional[Any]       =   py_tokenizer([raw_input_str]	,  return_tensors=SCREAMING_SNAKE_CASE_	,  add_special_tokens=SCREAMING_SNAKE_CASE_		).input_ids[0]
								self.assertListEqual(SCREAMING_SNAKE_CASE_	,  SCREAMING_SNAKE_CASE_		)







	def   _lowerCamelCase							(   self						:int		)						->					List[str]:
								__UpperCamelCase      :  Union[str, Any]       =   self._large_tokenizer
								# <mask_1> masks whole sentence while <mask_2> masks single word
								__UpperCamelCase      :  int       =   "<mask_1> To ensure a <mask_2> flow of bank resolutions."
								__UpperCamelCase      :  int       =   [2, 4_1_3, 6_1_5, 1_1_4, 3, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
								__UpperCamelCase      :  Dict       =   tokenizer([raw_input_str]	,  return_tensors=SCREAMING_SNAKE_CASE_		).input_ids[0]
								self.assertListEqual(SCREAMING_SNAKE_CASE_	,  SCREAMING_SNAKE_CASE_		)







	def   _lowerCamelCase							(   self						:List[Any]		)						->					int:
								__UpperCamelCase      :  Dict       =   self._large_tokenizer
								# The tracebacks for the following asserts are **better** without messages or self.assertEqual
								assert tokenizer.vocab_size == 9_6_1_0_3
								assert tokenizer.pad_token_id == 0
								assert tokenizer.eos_token_id == 1
								assert tokenizer.offset == 1_0_3
								assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_0_5
								assert tokenizer.unk_token == "<unk>"
								assert tokenizer.model_max_length == 1_0_2_4
								__UpperCamelCase      :  List[str]       =   "To ensure a smooth flow of bank resolutions."
								__UpperCamelCase      :  Optional[Any]       =   [4_1_3, 6_1_5, 1_1_4, 2_2_9_1, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
								__UpperCamelCase      :  Optional[int]       =   tokenizer([raw_input_str]	,  return_tensors=SCREAMING_SNAKE_CASE_		).input_ids[0]
								self.assertListEqual(SCREAMING_SNAKE_CASE_	,  SCREAMING_SNAKE_CASE_		)
								assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]		) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]







	@require_torch
	def   _lowerCamelCase							(   self						:Any		)						->					List[Any]:
								__UpperCamelCase      :  Union[str, Any]       =   ["This is going to be way too long." * 1_5_0, "short example"]
								__UpperCamelCase      :  List[Any]       =   ["not super long but more than 5 tokens", "tiny"]
								__UpperCamelCase      :  int       =   self._large_tokenizer(SCREAMING_SNAKE_CASE_	,  padding=SCREAMING_SNAKE_CASE_	,  truncation=SCREAMING_SNAKE_CASE_	,  return_tensors="pt"		)
								__UpperCamelCase      :  Optional[Any]       =   self._large_tokenizer(
								    text_target=SCREAMING_SNAKE_CASE_	,  max_length=5	,  padding=SCREAMING_SNAKE_CASE_	,  truncation=SCREAMING_SNAKE_CASE_	,  return_tensors="pt"		)

								assert batch.input_ids.shape == (2, 1_0_2_4)
								assert batch.attention_mask.shape == (2, 1_0_2_4)
								assert targets["input_ids"].shape == (2, 5)
								assert len(SCREAMING_SNAKE_CASE_		) == 2  # input_ids, attention_mask.







	@slow
	def   _lowerCamelCase							(   self						:List[str]		)						->					List[str]:
								__UpperCamelCase      :  int       =   {"input_ids": [[3_8_9_7_9, 1_4_3, 1_8_4_8_5, 6_0_6, 1_3_0, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 5_4_1_8_9, 1_1_2_9, 1_1_1, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 9_1_1_4, 1_4_7_8_7, 1_2_1, 1_3_2_4_9, 1_5_8, 5_9_2, 9_5_6, 1_2_1, 1_4_6_2_1, 3_1_5_7_6, 1_4_3, 6_2_6_1_3, 1_0_8, 9_6_8_8, 9_3_0, 4_3_4_3_0, 1_1_5_6_2, 6_2_6_1_3, 3_0_4, 1_0_8, 1_1_4_4_3, 8_9_7, 1_0_8, 9_3_1_4, 1_7_4_1_5, 6_3_3_9_9, 1_0_8, 1_1_4_4_3, 7_6_1_4, 1_8_3_1_6, 1_1_8, 4_2_8_4, 7_1_4_8, 1_2_4_3_0, 1_4_3, 1_4_0_0, 2_5_7_0_3, 1_5_8, 1_1_1, 4_2_8_4, 7_1_4_8, 1_1_7_7_2, 1_4_3, 2_1_2_9_7, 1_0_6_4, 1_5_8, 1_2_2, 2_0_4, 3_5_0_6, 1_7_5_4, 1_1_3_3, 1_4_7_8_7, 1_5_8_1, 1_1_5, 3_3_2_2_4, 4_4_8_2, 1_1_1, 1_3_5_5, 1_1_0, 2_9_1_7_3, 3_1_7, 5_0_8_3_3, 1_0_8, 2_0_1_4_7, 9_4_6_6_5, 1_1_1, 7_7_1_9_8, 1_0_7, 1], [1_1_0, 6_2_6_1_3, 1_1_7, 6_3_8, 1_1_2, 1_1_3_3, 1_2_1, 2_0_0_9_8, 1_3_5_5, 7_9_0_5_0, 1_3_8_7_2, 1_3_5, 1_5_9_6, 5_3_5_4_1, 1_3_5_2, 1_4_1, 1_3_0_3_9, 5_5_4_2, 1_2_4, 3_0_2, 5_1_8, 1_1_1, 2_6_8, 2_9_5_6, 1_1_5, 1_4_9, 4_4_2_7, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_3_9, 1_2_3_5, 2_7_9_9, 1_8_2_8_9, 1_7_7_8_0, 2_0_4, 1_0_9, 9_4_7_4, 1_2_9_6, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
								# fmt: on

								self.tokenizer_integration_test_util(
								    expected_encoding=SCREAMING_SNAKE_CASE_	,  model_name="google/bigbird-pegasus-large-arxiv"	,  revision="ba85d0851d708441f91440d509690f1ab6353415"	,  )




@require_sentencepiece
@require_tokenizers
class 					lowerCamelCase__				(							lowercase__	,		unittest.TestCase):




	'''simple docstring'''




	_A								=	PegasusTokenizer
	_A								=	PegasusTokenizerFast
	_A								=	True
	_A								=	True







	def   _lowerCamelCase							(   self						:Dict		)						->					Dict:
								super().setUp()

								# We have a SentencePiece fixture for testing
								__UpperCamelCase      :  Tuple       =   PegasusTokenizer(SCREAMING_SNAKE_CASE_	,  offset=0	,  mask_token_sent=SCREAMING_SNAKE_CASE_	,  mask_token="[MASK]"		)
								tokenizer.save_pretrained(self.tmpdirname		)







	@cached_property
	def   _lowerCamelCase							(   self						:Optional[int]		)						->					str:
								return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv"		)







	def   _lowerCamelCase							(   self						:List[str]	,  **a						:int		)						->					PegasusTokenizer:
								return PegasusTokenizer.from_pretrained(self.tmpdirname	,  **SCREAMING_SNAKE_CASE_		)







	def   _lowerCamelCase							(   self						:int	,  a						:Optional[int]		)						->					Dict:
								return ("This is a test", "This is a test")







	def   _lowerCamelCase							(   self						:Any		)						->					List[Any]:
								__UpperCamelCase      :  Optional[int]       =   self.rust_tokenizer_class.from_pretrained(self.tmpdirname		)
								__UpperCamelCase      :  Union[str, Any]       =   self.tokenizer_class.from_pretrained(self.tmpdirname		)
								__UpperCamelCase      :  int       =   (
								    "Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"
								    " <pad> <pad> <pad>"
								)
								__UpperCamelCase      :  Optional[Any]       =   rust_tokenizer([raw_input_str]	,  return_tensors=SCREAMING_SNAKE_CASE_	,  add_special_tokens=SCREAMING_SNAKE_CASE_		).input_ids[0]
								__UpperCamelCase      :  Union[str, Any]       =   py_tokenizer([raw_input_str]	,  return_tensors=SCREAMING_SNAKE_CASE_	,  add_special_tokens=SCREAMING_SNAKE_CASE_		).input_ids[0]
								self.assertListEqual(SCREAMING_SNAKE_CASE_	,  SCREAMING_SNAKE_CASE_		)







	@require_torch
	def   _lowerCamelCase							(   self						:Any		)						->					List[str]:
								__UpperCamelCase      :  int       =   ["This is going to be way too long." * 1_0_0_0, "short example"]
								__UpperCamelCase      :  List[str]       =   ["not super long but more than 5 tokens", "tiny"]
								__UpperCamelCase      :  Any       =   self._large_tokenizer(SCREAMING_SNAKE_CASE_	,  padding=SCREAMING_SNAKE_CASE_	,  truncation=SCREAMING_SNAKE_CASE_	,  return_tensors="pt"		)
								__UpperCamelCase      :  Optional[int]       =   self._large_tokenizer(
								    text_target=SCREAMING_SNAKE_CASE_	,  max_length=5	,  padding=SCREAMING_SNAKE_CASE_	,  truncation=SCREAMING_SNAKE_CASE_	,  return_tensors="pt"		)

								assert batch.input_ids.shape == (2, 4_0_9_6)
								assert batch.attention_mask.shape == (2, 4_0_9_6)
								assert targets["input_ids"].shape == (2, 5)
								assert len(SCREAMING_SNAKE_CASE_		) == 2  # input_ids, attention_mask.







	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					List[str]:
								__UpperCamelCase      :  Any       =   (
								    "This is an example string that is used to test the original TF implementation against the HF"
								    " implementation"
								)

								__UpperCamelCase      :  int       =   self._large_tokenizer(SCREAMING_SNAKE_CASE_		).input_ids

								self.assertListEqual(
								    SCREAMING_SNAKE_CASE_	,  [1_8_2, 1_1_7, 1_4_2, 5_8_7, 4_2_1_1, 1_2_0, 1_1_7, 2_6_3, 1_1_2, 8_0_4, 1_0_9, 8_5_6, 2_5_0_1_6, 3_1_3_7, 4_6_4, 1_0_9, 2_6_9_5_5, 3_1_3_7, 1]	,  )

"""simple docstring"""






import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
					import torch



@require_torch
@require_optimum
@slow
class 			lowercase					( unittest.TestCase		):






			def 					UpperCAmelCase							(self  :	Tuple			)       ->	Optional[int]:





				"""simple docstring"""


				lowerCAmelCase												=  '''hf-internal-testing/tiny-random-t5'''
				lowerCAmelCase												=  AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  tokenizer('''This is me'''      ,return_tensors='''pt'''			)

				lowerCAmelCase												=  model.to_bettertransformer()

				self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

				lowerCAmelCase												=  model.generate(**SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  model.reverse_bettertransformer()

				self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

				with tempfile.TemporaryDirectory() as tmpdirname:
					model.save_pretrained(SCREAMING_SNAKE_CASE_			)

					lowerCAmelCase												=  AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_			)

					self.assertFalse(
					    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

					lowerCAmelCase												=  model_reloaded.generate(**SCREAMING_SNAKE_CASE_			)
					self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)			)






			def 					UpperCAmelCase							(self  :	str			)       ->	Tuple:





				"""simple docstring"""


				lowerCAmelCase												=  '''hf-internal-testing/tiny-random-t5'''
				lowerCAmelCase												=  AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  model.to_bettertransformer()

				with tempfile.TemporaryDirectory() as tmpdirname:
					with self.assertRaises(SCREAMING_SNAKE_CASE_			):
						model.save_pretrained(SCREAMING_SNAKE_CASE_			)

					lowerCAmelCase												=  model.reverse_bettertransformer()
					model.save_pretrained(SCREAMING_SNAKE_CASE_			)


